Root Mean Square Error Of Prediction Research Articles

Multianalyte Detection with Metasurface-Based Midinfrared Microspectrometer.

Midinfrared (2.5-25 μm) spectroscopy is an ideal tool for identifying chemicals in a nondestructive manner. The traditional platform is a Fourier transform infrared (FTIR) spectrometer, but this is too bulky, expensive, and power-hungry for many applications. There is therefore a growing demand for small, lightweight, and cost-effective microspectrometers for use in the field. One emerging platform is the filter-array detector-array microspectrometer. It pairs a broadband detector array with a thin and rigid array of spectral filters to offer a robust, compact platform for real-time in situ sensing. However, most demonstrations have only focused on identifying a single chemical against a null sample, even though many applications would involve multianalyte detection. In this work, we show a rare attempt at simultaneously tracking multiple analytes with a metasurface filter-array microspectrometer. The metasurface consists of periodic lattices of subwavelength circular apertures in an aluminum layer to create an array of bandpass filters. The filter array is imaged with an off-the-shelf microbolometer via a reverse-lens imaging setup to simultaneously monitor the concentration of ethanol and methanol in gasoline. This represents an important application of fuel quality monitoring. Chemometric models (PLS and SVR) are trained and tested on gasoline blends with ethanol and methanol contents, both ranging from 0% to 20% v/v. A support vector machine regression (SVR) model with a cubic kernel was found to have the lowest combined prediction errors. The root-mean-square-error of prediction (RMSEP) for ethanol and methanol are 1.23% and 1.84% v/v; the corresponding pseudounivariate limit of detection is found to be 4.22% and 6.86% v/v, respectively. This work takes the emerging field of metasurface-based mid-infrared spectrometers from single- to multianalyte detection, thereby considerably expanding their range of potential applications.

Rapid Prediction of Moisture and Ash Content in Sungkai Leaves Herbal Tea (Peronema canescens Jack.) using NIR Spectroscopy

It is imperative to measure the chemical composition of Sungkai leaf herbal tea in order to produce high-quality goods that promote human health. The moisture and ash content of Sungkai leaf herbal tea are critical parameters for assessing the quality of herbal tea. This study aimed to evaluate an NIR spectroscopy method for quickly determining the moisture and ash content of Sungkai leaf herbal tea. Sungkai leaf herbal tea has a moisture content between 3.93% and 7.59%, and an ash content between 3.94% and 5.51%. We developed a calibration model using partial least squares (PLS) with several pretreatment methods. We split the data into calibration and prediction sets and performed an internal random cross-validation. A PLS calibration model with Rp2 = 0.86, a root means square error of prediction (RMSEP) of 0.30 (%), and a residual predictive deviation (RPD) of 2.76, performed exceptionally well at predicting the moisture content when the standard normal variate (SNV) pre-treatment was applied to the NIR spectra. The Savitzky-Golay derivative (a 9-point smoothing window, second-order polynomial, dg2) pre-treatment method also generated the best PLS calibration model for ash content determination, with Rp2 = 0.70, RMSEP = 0.16 (%), and RPD = 1.86. NIR spectroscopy can quickly determine the moisture and ash content of Sungkai leaf herbal tea, as suggested by these findings.

Raman spectroscopy coupled with the PLSR model: A rapid method for analyzing gamma-oryzanol content in rice bran oil

Rice bran oil (RBO) is widely used in food, nutraceutical, and cosmetic industries, due to its γ-oryzanol content, a key quality indicator. This study developed a rapid, non-destructive method for quantifying γ-oryzanol in RBO using Raman spectroscopy combined with partial least squares regression (PLSR). The optimal PLSR model, based on orthogonal signal correction (OSC)-pretreated data of Raman spectra from 800 to 1800 cm−1, demonstrated high accuracy with a strong R2-Pearson correlation coefficient of 0.9827 and low root mean square error of prediction (RMSEP) of 0.5314. Principal component analysis (PCA) of OSC-pretreated data showed improved sample grouping by concentration of γ-oryzanol compared to untreated data. Additionally, Bland-Altman plots comparing results from Raman and HPLC methods showed random scatter within ±2 SD of the mean difference, confirming the method's reliability. This study indicates that Raman spectroscopy can serve as a reliable method for determining γ-oryzanol content in RBO products within the related industries.

Spectrophotometric determination of olanzapine, fluoxetine HCL and its impurity using univariate and chemometrics methods reinforced by latin hypercube sampling: Validation and eco-friendliness assessments

Novel univariate and chemometrics-aided UV spectrophotometric methods were tailored to undergo the fundamentals of green and white analytical chemistry for the simultaneous estimation of a ternary mixture of olanzapine (OLA), fluoxetine HCL (FLU), and its toxic impurity 4-(Trifluoromethyl) phenol (FMP) without any prior separation. The dual-wavelength ratio spectrum univariate method was used to determine OLA and FLU in the presence of FMP in the range of (4–20) and (5–50) μg/ml, respectively. In compliance with the International Conference on Harmonization (ICH) standards, the technique was validated and established Remarkable accuracy (98–102%) and precision (< 2%) with limits of quantification (LOQs) of 0.432 and 2.002 μg/ml, respectively. Partial least squares (PLS) and artificial neural networks (ANNs) are chemometric methodologies that have advantages over the univariate method and use significant innovations employing Latin hypercube sampling (LHS), allowing the generation of a reliable validation set to guarantee the effectiveness and sustainability of these models. The concentration ranges used were (2–20), (2–20), and (5–50) μg/ml; for PLS, the LOQs were 0.602, 0.508, and 1.429 μg/ml, and the root mean square errors of prediction (RMSEPs) were 0.087, 0.048, and 0.159 for OLA, FMP, and FLU, respectively; and for ANNs, the LOQs were 0.551, 0.465, and 0.965 μg/ml, with RMSEPs of 0.056, 0.047, and 0.087 for OLA, FMP, and FLU, respectively. The developed methods yield a greener National Environmental Methods Index (NEMI) with an eco-scale assessment (ESA) score of 90 and a complementary Green Analytical Procedure Index (complex GAPI) in quadrants with an analytical greenness metric (AGREE) score of 0.8. The Red‒Green–Blue 12 algorithm (RGB 12) scored 88.9, outperforming on reported methods and demonstrating widespread practical and environmental approval. Statistical analysis revealed no noteworthy differences (P > 0.05) among the proposed and published techniques. Both pure powders and pharmaceutical capsules were analyzed via these methods.

Zinc determination in common beans by pXRF: An easy and versatile calibration strategy applied to biofortification studies

Zinc deficiency is observed in millions of individuals, especially in underdeveloped countries. Therefore, agronomical strategies have been implemented to mitigate this public health issue, mainly by the biofortification of staple food crops, e.g., common beans, one of the most consumed leguminous grains worldwide. Accurate Zn determination by an analytical method is one of the most relevant steps in these initiatives. This study evaluated three calibration methods for Zn determination by portable X-ray Fluorescence Spectrometry (pXRF) in pelletized common bean samples. A Ti/Al primary filter and a 10-second irradiation time were selected as optimized operating conditions. The sample with the highest Zn content was submitted to acid extraction with diluted HCl and HNO3 to obtain synthetic blanks. Increasing amounts of the original sample were mixed with the obtained blanks to prepare the calibration curves standards. An additional calibration model with plant-based CRMs was also evaluated. Zinc mass fractions calculated using the calibration model of HCl-extracted and mixed samples showed statistical agreement with the reference data as demonstrated by the Student t-test at a 95 % confidence level. The linear correlation factor (r) was greater than 0.99, with a detection limit as low as 2.23 mg kg−1. The obtained calibration model also exhibited high prediction capability, as revealed by the low RMSEP (Root Mean Square Error of Prediction), i.e., 2.16 mg kg−1. We concluded that pXRF is an attractive and cost-effective method for direct, quick, and environmentally friendly Zn determination in common beans.

Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) analysis of human nails: Implications for age determination in forensics.

A person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT-IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS-R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS-DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof-of-concept study highlights the potential of ATR FT-IR spectroscopy as a non-destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications.

Combinatorial Order Pre-processing Search (COPS): A new pre-processing strategy for large-scale interpretable data analysis in process analytical technologies

Combinatorial Order Pre-processing Search (COPS), a novel approach for optimizing data pre-processing is proposed in this work. Unlike simultaneous hyperparameter optimization, COPS employs a priori optimization to reduce computational time while refining the search space for preprocessing sequences and combinations. It allows for setting a maximum number of pre-processing methods, while efficiently searching through combinations of methods with chemically relevant knowledge. In this work, 67 calibration datasets across various analytical techniques, including fluorescence spectroscopy, gas chromatography (GC), near-infrared spectroscopy (NIR), mid-infrared spectroscopy (MIR), visible-near-infrared spectroscopy (Vis-NIR), Raman spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and voltammetry were evaluated. COPS yielded significant improvements over existing methodologies based on design of experiment and compounded pre-processing approaches. The COPS outperformed the other methods, resulting in an average root mean square error of prediction (RMSEP) reduction of 31.7%, while also reduced the complexity (number of latent variables) of the model which allows for easier interpretation. This underscores the importance of combinatorial order set theory for the search of pre-processing method combinations (without fixing the sequence of pre-processing methods) to enhance model performance and interpretation. The novel COPS approach can be employed in process analytical technology (such as inline, online or at-line chemical sensing analytics) to enhance predictive accuracy and operational efficiency, fundamentally transforming the quality and reliability of chemical process monitoring and control.

A novel Kalman smoothing (Ks) − Long Short-Term Memory (LSTM) hybrid model for filling in short- and long-term missing values in significant wave height

Continuous recordings of significant wave height (SWH) are of significant importance to facilitate the development and application of wave energy assessments. However, SWH recording is often interrupted by a variety of factors, including severe weather, apparatus failure, etc. The presence of short-term intermittent and long-term continuous missing values is recognized as a significant challenge for the accurate analysis and energy assessment of wave data. To effectively fill in the short- and long-term missing values simultaneously, a hybrid Kalman smoothing (KS) − Long Short-Term Memory (LSTM) model, which is applied to fill in SWH for the first time, is proposed. Initially, the short-term intermittent missing values, with missing ratios of 10%-50%, are filled by using the KS method, achieving a root mean square error (RMSE) as low as 0.082. Based on short-term missing values addressed by KS, the LSTM model is introduced to effectively predict the long-term continuous missing values. The maximum RMSE reduction rate of KS-LSTM is reduced by 49.6%, 59.4%, and 57.8% compared to KS-ARIMA, LSTM, and ARIMA, respectively, within the short-term intermittent missing ratios of 10%-50%. The maximum reduction in mean square error (MSE) is observed to be 71.4%, 83.6%, and 82.1%. Similarly, the maximum reduction in mean absolute error (MAE) is 74.6%, 61.1%, and 55.5%. The lowest prediction RMSE of long-term missing values by KS-LSTM is only 0.091, which demonstrates that the effectiveness of filling in both short-term and long-term missing values simultaneously by the KS-LSTM.

Determination of terpene-based VOCs emitted from pine wood using NIR spectroscopy

ABSTRACT In the search for a quick and less labor-intensive method for determining terpene emissions of high emitting wood like Pinus sylvestris, this study utilized near-infrared spectroscopy (NIR) combined with multivariate analysis methods and compared it to the standard measurement method according to the EN 16516 standard. The spectra were measured on the surface of solid-wood samples and correlated with the simultaneously determined air concentrations of terpenes. For α-pinene and 3-carene, prediction models were created using partial least squares regression (PLSR). Both work with determination coefficients of 0.77 and a Root Mean Square Error of Prediction (RMSEP) of 12% of the maximum emissions achievable in this study. A model based on the sum of the two terpenes was built and tested to determine the predictive capability of multiple similar substances simultaneously from the NIR recording. The model performs slightly worse, with a determination coefficient of 0.71 and RMSEP of 13%. Despite continuous expansion of the datasets for multiple models, no better metrics were achieved, likely because of the heterogeneous structure of wood when scanning a solid-wood surface. Systematic deviations were observed in particularly low reference ranges, which may indicate non-linearity in the relationship between terpenes and the NIR signal.

A combination of XGBoost and neural network in LIBS spectrum processing for precise determination of critical elements in 620 iron ore samples of various origins

China is the worldwide largest importer of iron ores for supplying its iron and steel industries. The necessary inspections of iron ores not only concern total iron content but also involve minor elements, such as silicium, aluminum, magnesium, and calcium. Laser-induced breakdown spectroscopy (LIBS) promises in situ and on-site detection and analysis capabilities, which is required for efficient, reliable, and large-scale impartation inspections. Such opportunity represents at the same time, a challenge for the technique in terms of the quantitative analysis ability. A fundamental issue corresponds to the matrix effect in LIBS analysis due to a large diversity of iron ores in terms of mineralogical and chemical compositions. Demonstrated as effective to deal with the matrix effect in LIBS analyses of geological materials, the machine learning approach needs to be implemented within an optimized data processing pipeline, combining algorithms carefully chosen and tested. In this work, we developed a sequentially connected combination of an extreme gradient boosting (XGBoost) model and a back propagation neural network (BPNN) model, to effectively fit the functional relation between the concentrations and the spectra. The specificity of the approach consists in fitting the linear, low-order nonlinear, and high-order nonlinear terms in a sequential way to substantially improve the model prediction performances. The method has been developed and tested with a significant set of 620 collected iron ore samples of eight production countries and 35 different commercial brands, with a wide range of matrices in terms of mineralogical and chemical compositions. The samples underwent prior characterization using conventional laboratory analysis methods to establish the reference elemental concentrations. The results from this necessary step of laboratory investigation, with root mean square error for prediction (RMSEP) of 0.407 wt%, 0.372 wt%, 0.085 wt%, 0.131 wt%, and 0.114 wt%, respectively for total iron (TFe), SiO2, Al2O3, MgO, and CaO, show the potential for on-site inspections.

A novel variable selection algorithm based on neural network for near-infrared spectral modeling

BackgroundPartial least squares (PLS) is a widely used technique for modeling spectral data. Researchers have developed numerous PLS-based variable selection algorithms to enhance model predictive ability and interpretability. In recent years, as neural network technology has advanced, these algorithms have been increasingly applied to spectral data modeling. However, current research on neural network modeling tends to prioritize network structure over variable selection. ResultsOur study introduces a neural network-based variable selection algorithm called VSNN (Variable Selection based on Neural Network). By iteratively eliminating unimportant variables using an exponentially decreasing function (EDF), the algorithm achieves the selection of variables in spectral data. VSNN can easily integrate different types of neural networks. In this study, we analyzed the impact of neural network types, activation functions, and variable importance vectors on algorithm performance. We tested the algorithm on four datasets: corn moisture, corn oil, tablets, and meat. The results indicate that VSNN significantly enhances the predictive ability of the model compared to partial least squares (PLS), neural networks (NN), and Joint Mutual Information Maximisation (JMIM). Specifically, non-linear activation functions markedly improve performance on non-linear meat datasets. Compared to PLS, the Root Mean Square Error of Prediction (RMSEP) values for the four datasets—corn moisture, corn oil, tablets, and meat—decreased from 0.0409, 0.0728, 3.97, and 3.2 to 0.002, 0.0236, 3.12, and 0.36, respectively, after applying the VSNN variable selection algorithm. SignificanceVSNN can serve as a versatile framework to enhance variable selection, modeling, and predictive performance by adapting neural network types and variable importance evaluation indicators. As machine learning technology advances, the strength of VSNN is poised to increase. This study highlights the potential of VSNN as an effective algorithmic framework for variable selection in spectroscopy applications.

Comparative Analysis of XGB, CNN, and ResNet Models for Predicting Moisture Content in Porphyra yezoensis Using Near-Infrared Spectroscopy.

This study explored the performance and reliability of three predictive models-extreme gradient boosting (XGB), convolutional neural network (CNN), and residual neural network (ResNet)-for determining the moisture content in Porphyra yezoensis using near-infrared (NIR) spectroscopy. We meticulously selected 380 samples from various sources to ensure a comprehensive dataset, which was then divided into training (300 samples) and test sets (80 samples). The models were evaluated based on prediction accuracy and stability, employing genetic algorithms (GA) and partial least squares (PLS) for wavelength selection to enhance the interpretability of feature extraction outcomes. The results demonstrated that the XGB model excelled with a determination coefficient (R2) of 0.979, a root mean square error of prediction (RMSEP) of 0.004, and a high ratio of performance to deviation (RPD) of 4.849, outperforming both CNN and ResNet models. A Gaussian process regression (GPR) was employed for uncertainty assessment, reinforcing the reliability of our models. Considering the XGB model's high accuracy and stability, its implementation in industrial settings for quality assurance is recommended, particularly in the food industry where rapid and non-destructive moisture content analysis is essential. This approach facilitates a more efficient process for determining moisture content, thereby enhancing product quality and safety.

Rapid Lactic Acid Content Detection in Secondary Fermentation of Maize Silage Using Colorimetric Sensor Array Combined with Hyperspectral Imaging

Lactic acid content is a crucial indicator for evaluating maize silage quality, and its accurate detection is essential for ensuring product quality. In this study, a quantitative prediction model for the change of lactic acid content during the secondary fermentation of maize silage was constructed based on a colorimetric sensor array (CSA) combined with hyperspectral imaging. Volatile odor information from maize silage samples with different days of aerobic exposure was obtained using CSA and recorded by a hyperspectral imaging (HSI) system. Subsequently, the acquired spectral data were subjected to preprocessing through five distinct methods before being modeled using partial least squares regression (PLSR). The coronavirus herd immunity optimizer (CHIO) algorithm was introduced to screen three color-sensitive dyes that are more sensitive to changes in lactic acid content of maize silage. To minimize model redundancy, three algorithms, such as competitive adaptive reweighted sampling (CARS), were used to extract the characteristic wavelengths of the three dyes, and the combination of the characteristic wavelengths obtained by each algorithm was used as an input variable to build an analytical model for quantitative prediction of the lactic acid content by support vector regression (SVR). Moreover, two optimization algorithms, namely grid search (GS) and crested porcupine optimizer (CPO), were compared to determine their effectiveness in optimizing the parameters of the SVR model. The results showed that the prediction accuracy of the model can be significantly improved by choosing appropriate pretreatment methods for different color-sensitive dyes. The CARS-CPO-SVR model had better prediction, with a prediction set determination coefficient (RP2), root mean square error of prediction (RMSEP), and a ratio of performance to deviation (RPD) of 0.9617, 2.0057, and 5.1997, respectively. These comprehensive findings confirm the viability of integrating CSA with hyperspectral imaging to accurately quantify the lactic acid content in silage, providing a scientific and novel method for maize silage quality testing.

Bootstrap-integrated machine learning techniques for the calibration of near-infrared (NIR) spectra

Near-infrared (NIR) spectroscopy system is frequently used in food production because of its superior characteristics. Establishing models on spectral data for prediction has always been of interest. However, no satisfactory model has been developed on different data types for accuracy and reliability. In this study, an approach strategy is reported for the calibration of NIR spectra by utilizing a combination of the bootstrap technique and an ensemble method. The approach comprises three steps. First, data are resampled to create bootstrap samples. Second, four calibration models are applied to the grouped data: partial least squares regression (PLSR), support vector regression (SVR), backpropagation neural network (BPNN), and principal component analysis-backpropagation neural network (PCA-BPNN). Finally, predictions obtained after the calibration are combined using the ensemble method. The data studied include 215, 32, and 540 samples, which were characterized as hyperspectral, small sample, and categorical data, respectively. Root mean square error of prediction (RMSEP), R squared ( R 2 ), and residual prediction deviation (RPD) were used to describe the accuracy. Coverage probability of the prediction interval (PICP) and normalized average prediction interval width (NMPIW) were utilized to evaluate the reliability. The accuracies of the methods exhibited the following order: the proposed method > PLSR > SVR > BPNN ≈ PCA-BPNN. The results confirm that the reported model achieved satisfactory accuracy and was more reliable than the single calibration models.

Model evaluation: The misuse of statistical techniques when evaluating observations versus predictions

Mathematical modellers, decision support developers, statisticians, and students evaluate the differences between observed and model predicted values. When evaluating models, it is far too easy to conduct model evaluation by fitting a linear regression to the data. In this paper, steps are presented on ‘how to’ evaluate a model using deviance metrics rather than reporting r2 from fitting a linear regression. The paper aims to provide sound reasoning, with data, against using r2. The paper addresses five arguments, previously put forward, for not fitting a linear regression when conducting model evaluation: i) Misapplication of regression; ii) Ambiguity of null hypothesis tests; iii) Lack of sensitivity; iv) Fitted line is irrelevant to validation; and v) Violation of regression assumptions. Statistical, deviance, and quality control metrics are outlined. Three models using the BeefSpecs drafting tool are reported in this paper. Each model (n = 80) had an r2 of 0.43. A mean bias of 0.06, -2.90, and -0.11 mm, and a root mean square error of prediction (RMSEP) of 1.72, 3.37, and 3.70 mm for models 1, 2, and 3, respectively. A modelling efficiency (MEF) of 0.39, -1.34, and -1.83, and 91, 51, and 56% of predictions within upper and lower quality control limits for models 1, 2, and 3, respectively. These metrics highlight the pitfall of reporting r2 from using regression. Minimum recommended steps of ‘how to’ conduct model evaluation are: a plot of the residuals with quality control limits and a table of metrics including mean observed, predicted and bias, RMSEP, and MEF.

PSXI-23 Development of sub-models to estimate protein requirements and supply of lactating dairy cows using machine learning algorithms

Abstract The objectives of this study were to develop sub-models for predicting protein requirements and supply, encompassing 1) net protein for maintenance (NPm), 2) lactation (NPl), 3) rumen undegradable protein (RUP), and 4) duodenal microbial nitrogen (MicN) from the feed protein. The dataset used in this study was constructed by integrating in vivo experimental data collected from open databases (the National Animal Nutrition Program) and articles (Journal of Dairy Science), which includes a total of 1,779 observations from 436 publications. In the development of the model, animal information and feed chemical components were used as candidate variables, and two types of machine learning algorithms, Random Forest Regression (RFR) and Support Vector Regression (SVR) were employed. After testing, the following predictors were selected for predicting: 1) NPm: body weight (BW) and dry matter intake (DMI), 2) NPl: BW, DMI, days in milk (DIM), and dietary organic matter (OM) and crude protein (CP) contents, 3) RUP: DIM, DMI, dietary DM content, and CP fraction intake (B and C), and 4) MicN: DIM, DMI, DM, dietary neutral detergent fiber (NDF) content, CP fraction intake (A, B, and C). The selected models were assessed using a cross-validation method with the following statistical metrics including the coefficient of determination (R2), root-mean-square error of prediction (RMSEP), residual analyses, and concordance correlation coefficient (CCC). For the RUP and MicN models, they were compared with the NASEM (2021) model. In predicting NPm, both SVR and RFR algorithms demonstrated increased precision (R2 = 0.965 vs. 0.969) and accuracy (RMSEP = 9.7 vs. 9.2 g/d); however, during residual analysis, the RFR model showed a statistically significant slope bias (P &amp;lt; 0.05). In the NPl prediction, the RFR algorithm showed slightly greater performance compared with SVR (R2 = 0.864 vs. 0.814 and RMSEP = 86.7 vs. 98.5 g/d). However, similar to the NPm prediction, the RFR model displayed a statistically significant slope bias (P &amp;lt; 0.05). In the supply model, the RFR model exhibited the greatest precision and accuracy in predicting RUP (R2 = 0.60, RMSEP = 0.326 kg/d, and CCC = 0.71) without any biases. This model achieved a 2.37-fold increase in R2 and a decrease of 0.111 kg/d in RMSEP compared with the NASEM model. As for MicN prediction, the SVR model performed the best (R2 = 0.76, RMSEP = 42.4 g/d, and CCC = 0.86) without biases. This model attained a 19-times improvement in R2 and a reduction of 38.7 g/d in RMSEP when compared with the NASEM model. In conclusion, the models developed using machine learning algorithms can be helpful for accurately and precisely predicting protein requirements and supply based on animal information and the chemical composition of feed.

Surface-enhanced Raman spectroscopy (SERS) for the diagnosis of acute myocardial infarction (AMI) using blood serum samples.

Acute myocardial infarction (AMI) is a serious medical condition generally known as heart attack, which is caused by the decreased or completely blocked blood flow to a part of the heart muscle. It is a significant cause of both mortality and morbidity throughout the world. Cardiac troponin-I (cTnI) is an important biomarker at different stages of AMI and is one of the most specific and widely used cardiac skeletal muscle proteins. Delays in medical treatment and inaccurate diagnosis might be the main cause of death of AMI patients. To overcome the death rate of AMI patients, early diagnosis of this disease is crucial. In the current study, surface-enhanced Raman spectroscopy (SERS) is employed for the characterization and diagnosis of this disease using blood serum samples from 49 clinically confirmed acute myocardial infarction (AMI) patients and 17 healthy persons. Silver nanoparticles (AgNPs) are used as the SERS substrate for the recognition of characteristic SERS spectral features, differentiating between healthy and AMI-positive samples. The acute myocardial infarction-positive blood serum samples reveal remarkable differences in spectral intensities at 534, 697, 744, 835, 927, 941, 988, 1221, 1303, 1403, 1481, 1541, 1588 and 1694 cm-1. For the differentiation and quantitative analysis of the SERS spectra, multivariate chemometric tools (including principal component analysis (PCA) and partial least squares regression (PLSR)) are employed. A PLSR model established on the basis of differentiating the SERS spectral features is found to be helpful in the prediction of the levels of cardiac troponin-I (cTnI) in the blood serum samples with the root mean square error of calibration (RMSEC) value of 2.98 ng mL-1 and root mean square errors of prediction (RMSEP) value of 3.98 ng mL-1 for S7.

Towards Sustainable and Dynamic Modeling Analysis in Korean Pine Nuts: An Online Learning Approach with NIRS.

Due to its advantages such as speed and noninvasive nature, near-infrared spectroscopy (NIRS) technology has been widely used in detecting the nutritional content of nut food. This study aims to address the problem of offline quantitative analysis models producing unsatisfactory results for different batches of samples due to complex and unquantifiable factors such as storage conditions and origin differences of Korean pine nuts. Based on the offline model, an online learning model was proposed using recursive partial least squares (RPLS) regression with online multiplicative scatter correction (OMSC) preprocessing. This approach enables online updates of the original detection model using a small amount of sample data, thereby improving its generalization ability. The OMSC algorithm reduces the prediction error caused by the inability to perform effective scatter correction on the updated dataset. The uninformative variable elimination (UVE) algorithm appropriately increases the number of selected feature bands during the model updating process to expand the range of potentially relevant features. The final model is iteratively obtained by combining new sample feature data with RPLS. The results show that, after OMSC preprocessing, with the number of features increased to 100, the new online model's R2 value for the prediction set is 0.8945. The root mean square error of prediction (RMSEP) is 3.5964, significantly outperforming the offline model, which yields values of 0.4525 and 24.6543, respectively. This indicates that the online model has dynamic and sustainable characteristics that closely approximate practical detection, and it provides technical references and methodologies for the design and development of detection systems. It also offers an environmentally friendly tool for rapid on-site analysis for nut food regulatory agencies and production enterprises.

Development of low-cost portable spectrometer equipped with 18-band spectral sensors using deep learning model for evaluating moisture content of rubber sheets

While the choice of spectrometer can vary depending on its intended use, the increased cost of high-performance spectrometers may not be justified in certain applications. Therefore, this research developed an affordable and portable device using 18-band spectral sensors incorporating a deep learning model for accurately determining the moisture content in rubber sheets. A set of 286 rubber sheets was randomly separated into two categories: 200 for model calibration and 86 for model validation. In the calibration process, the spectral data were calibrated using a one-dimensional convolutional neural network (1D-CNN) and then were compared with a recognized linear model using partial least squares regression (PLSR). The experiments revealed the exceptional performance of the 1D-CNN model in predicting the moisture content of rubber sheets, outperforming the PLSR model. The 1D-CNN model had a better prediction accuracy, with a coefficient of determination (R2) of 0.962, a root mean squared error of prediction (RMSEP) of 0.410 %, a prediction-to-deviation ratio (RPD) of 5.2, and an error range ratio (RER) of 18.0. A portable device was constructed by incorporating the 1D-CNN model into a 32-bit microcontroller, which was embedded within the measurement device. During testing of the instrument, the results indicated that its predictive performance did not differ significantly from that of the primary calibration model. Therefore, it could be concluded that the designed instrument was capable of accurately measuring the moisture content of rubber sheets and is suitable for field use due to its portability and cost-effectiveness.

Bias correction and ensemble techniques in statistical downscaling model for rainfall prediction using Tweedie-LASSO in West Java, Indonesia

Rainfall is a climate element with high variations in space and time scales, so it is not easy to predict. One way to predict rainfall is statistical downscaling (SD). SD can predict local rainfall based on Global Circulation Model (GCM) data. The Decadal Climate Prediction Project (DCPP), one of the GCMs, originates from adjacent grids and experiences multicollinearity problems. Rainfall as a response variable is Tweedie Compound Poisson Gamma (TCPG) distribution data because it has a discrete component (rainfall events) and a continuous component (rainfall intensity), so SD modelling will be carried out using Tweedie-LASSO. This research aims to compare the performance of bias correction and ensemble methods in SD in predicting rainfall in West Java, Indonesia. Bias correction uses Empirical Quantile Mapping (EQM) with CHIRPS data, and the ensemble method uses a stacking technique with Random Forest (Stacking-RF) due to the varied characteristics of DCPP model sources. Evaluation results using Root Mean Square Error Prediction (RMSEP) and correlation coefficient show that bias correction improves single-model performance but not ensemble models. Besides that, ensemble models outperform single models both before and after bias correction. The combination of bias correction and ensemble modelling can be recommended when conducting SD to enhance the prediction capability of rainfall at stations and other areas.